Sowing the seeds for sustainability: A business model innovation perspective on artificial intelligence in green technology startups

Philip Jorzik, Jerome L. Antonio, Dominik K. Kanbach, Andreas Kallmuenzer and Sascha Kraus

Technological Forecasting and Social Change, 2024, vol. 208, issue C

Abstract: In today's data-driven era, ubiquitous concern about environmental issues pushes more startups to engage in business model innovation that promotes environmentally friendly technologies. The goal of these startups is to create technology-based products and services that enhance environmental sustainability. In this context, artificial intelligence promises to be a key instrument to create, capture, and deliver value. However, the existing literature lacks a deep understanding of how startups using AI innovate their business models to achieve a positive environmental impact. Therefore, this paper investigates how green technology startups utilize AI from a business model innovation perspective for environmental sustainability. We conduct a qualitative, exploratory multiple-case study using the Eisenhardt methodology, based on interview data analyzed using qualitative content analysis. We derive five predominant manifestations for AI-driven business model innovation and identify archetypical connections between business model dimensions. Further, we establish three overarching archetypical associations among the cases. In doing so, we contribute to theory and practice by providing a deeper account of how green technology startups attempt to maximize their positive environmental impact through AI. The results of this study also highlight how business model innovation driven by AI can support society in securing a more environmentally sustainable future.

Keywords: Business model innovation; Artificial intelligence; Green technology startups; Sustainability; Entrepreneurship; Startups (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:208:y:2024:i:c:s0040162524004517

DOI: 10.1016/j.techfore.2024.123653

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